[HTML][HTML] A systematic literature review on the use of federated learning and bioinspired computing
R Marin Machado de Souza, A Holm, M Biczyk… - Electronics, 2024 - mdpi.com
Federated learning (FL) and bioinspired computing (BIC), two distinct, yet complementary
fields, have gained significant attention in the machine learning community due to their …
fields, have gained significant attention in the machine learning community due to their …
[HTML][HTML] Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?
The integration of AI and ML into energy forecasting is crucial for modern energy
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
management. Federated Learning (FL) is particularly noteworthy because it enhances data …
Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives
Current approaches to developing artificial intelligence (AI) models for widespread
glaucoma screening have encountered several obstacles. First, glaucoma is a complex …
glaucoma screening have encountered several obstacles. First, glaucoma is a complex …
SoK: Demystifying privacy enhancing technologies through the lens of software developers
In the absence of data protection measures, software applications lead to privacy breaches,
posing threats to end-users and software organisations. Privacy Enhancing Technologies …
posing threats to end-users and software organisations. Privacy Enhancing Technologies …
Privacy-Preserving Federated Learning for Intrusion Detection in IoT Environments: A Survey
With the rapid development of artificial intelligence and a new generation of network
technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents …
technologies, the Internet of Things (IoT) is expanding worldwide. Malicious agents …
Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects
Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …
Intelligence (AI), enabling collaborative model training across distributed devices while …
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research
Group fairness in machine learning is a critical area of research focused on achieving
equitable outcomes across different groups defined by sensitive attributes such as race or …
equitable outcomes across different groups defined by sensitive attributes such as race or …
Multimodal Federated Learning in Healthcare: a review
J Thrasher, A Devkota, P Siwakotai… - arXiv preprint arXiv …, 2023 - arxiv.org
Recent advancements in multimodal machine learning have empowered the development
of accurate and robust AI systems in the medical domain, especially within centralized …
of accurate and robust AI systems in the medical domain, especially within centralized …
Evolving Topics in Federated Learning: Trends, and Emerging Directions for IS
Federated learning (FL) is a popular approach that enables organizations to train machine
learning models without compromising data privacy and security. As the field of FL continues …
learning models without compromising data privacy and security. As the field of FL continues …
The Performance Analysis of Federated Learning Methods for IoT with Big Data
A Govindaram, A Jegatheesan - 2024 11th International …, 2024 - ieeexplore.ieee.org
ML that creates a global framework by gathering knowledge from a number of different
dispersed edge clients. FL allows on-device training, keeps client information in private, and …
dispersed edge clients. FL allows on-device training, keeps client information in private, and …